We investigate the effect of X-ray OOD data on the performance of an existing, “radiologist-level” chest X-ray classifier.
We trained the CheXnet model [1]} on the Chest X-ray 14 (CXR14) data set [2]}.
The data set consists of 112,120 frontal chest radiographs with 14 annotated chest pathologies (see Figure REF  c for a list of labels).
The model achieved a mean area the under the receiver operating characteristic curve (AUC) of 83 % when tested without OOD images, as shown in Table Appendix REF .
